Non-monotonic Reasoning and the Reversibility of Belief Change
Artificial Intelligence
2013-03-26 v1
Abstract
Traditional approaches to non-monotonic reasoning fail to satisfy a number of plausible axioms for belief revision and suffer from conceptual difficulties as well. Recent work on ranked preferential models (RPMs) promises to overcome some of these difficulties. Here we show that RPMs are not adequate to handle iterated belief change. Specifically, we show that RPMs do not always allow for the reversibility of belief change. This result indicates the need for numerical strengths of belief.
Keywords
Cite
@article{arxiv.1303.5723,
title = {Non-monotonic Reasoning and the Reversibility of Belief Change},
author = {Daniel Hunter},
journal= {arXiv preprint arXiv:1303.5723},
year = {2013}
}
Comments
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)